Designing an ideal anticancer molecule

Hypotheses

  1. Since cancer cells are different from normal cells in many aspects, there exists a well-defined molecular mechanism differentiating cancer (immortal) cells from adult (differentiated, mortal) cells. We will call it Cancer-Specific Molecular Machinery (CSMM). By definition, CSMM is common for many (perhaps, all) types of cancer.

  2. CSMM is present not only in tumor cells and cancer cell lines, but also in cancer stem cells, and it different from the pluripotency machinery in “normal” stem cells.

  3. CSMM can be targeted by small molecule drugs. In other words, it is possible to find a small molecule that will be toxic for most/all cancer cells and much less toxic for normal cells.

  4. Among the >100,000 molecules NCI has already tested on the NCI-60 cell panel, some of the molecules are already targeting CSMM, albeit inefficiently.

  5. By bioinformatic analysis of NCI-60 datasets, it is possible to identify a group of molecules enriched with the CSMM-targeting molecules. We will call this group CSMM-targeting candidates.

  6. Based on the identified CSMM-targeting candidates and NCI-60 data, it is possible to build empirical models of CSMM-targeting molecules with sufficient predictive power for meaningful prediction of their Efficacy E, Unspecificity U and Safety S.

  7. Based on the above predictions for millions of molecules generated in silico based on the best CSMM-targeting candidates, it is possible to identify most promising candidates.

Aims

  1. To predict novel small molecule candidates which should be:
    • active (in terms of \(IC_{50}/LC_{50}\)),
    • unspecific (similar \(IC_{50}/LC_{50}\) across different cancer cell lines),
    • safe (non-toxic).

  2. Synthesize these candidate molecules and test their activity and unspecificity on cell panels, including the NCI-60 panel.

  3. Provided E and U are high, find the protein targets and mechanism(s) of action of these molecules using the FITExP method (expression-based analysis).

  4. Confirm the protein targets of these molecules using the SITExP method (solubility-based analysis).

  5. Based on these results, test the hypothesis 1.

  6. Determine the toxicity of the candidate molecules on normal stem cells, thus testing the hypothesis 2.

  7. Provided the targets and the mechanisms are novel and look like a plausible CSMM candidate, determine experimentally the safety S of the novel molecules, thus testing the hypotheses 3-7.

Methods

  1. “Seed” selection (ACh).

    1. DTP-NC60 dataset will be used as a starting point for candidate molecule identification. There are more than 100,000 small molecules tested, ~33,000 has measured IC50 values in at least 50 of 60 cell lines. For each of the compound in dataset we will calculate following characteristics:
      • Efficacy E, as \(-log(IC_{50})\) averaged over cell lines.
      • Unspecificty U, difference between the max and mean \(-log(IC_{50})\).
      • Safety S, using GUSAR’s Acute Mice Toxicity model.
    2. We will define the figure-of-merit function Q(E,U,S) assingns then highest value to the ”best” molecules.
    3. Then we will select 500-1000 best candidates with the highest Q values.

  2. Clustering by MOA (ACh).

    For each of the selected candidate molecules, an activity spectrum (AS) will be calculated using PASS software. Then the molecules will be clustered by similarity (correlation or covariation) of AS. One or two most consistent and large clusters will be selected (expected to comprise 20-50 molecules each).

  3. Cluster enrichment and candidate selection (ACh).

    1. Each of the selected clusters will be enriched by structurally similar molecules from the DTP-NC60 dataset.
    2. For each of the selected clusters, we will build, using PASS and GUSAR, models predicting E and U. Toxicity model already exists (GUSAR).
    3. For each cluster, we will select molecules from the Chembl database that pass the structural similarity threshold (will be defined for each cluster).
    4. For thus selected molecules E, U and S will be calculated, as well as Q.
    5. Best hits (max Q) will be selected in each cluster. Based on their structural features and AS, it will be analyzed whether they represent essentially the same structure or different structures. In the first case, simplest structure will be chosen as “seed”. In the latter case, several different simplest structures will be chosen as seeds.

  4. Experimental validation (AS).

    The seeds from each cluster will be ordered (or synthesized) and tested in the lab on at least three different cancer cell lines (HCT116, RKO and A375(?)). Experimental IC50 values will be determined. All hits found to be active will be sent to NCI-60 for testing on 60 cell lines.

  5. Targets and MOA identification (AS and ACh).

    FITExP and SITExP methods will be used to identify the targets and MOA of the verified hits.

  6. Novel target validation (AS).

    Novel target candidates identified by UTIExP will be validated using siRNA.

  7. Safety of novel molecules (AS; outsourced).

    Determine the toxicity of the candidate molecules on normal stem cells. Safety of novel molecules for animals will be tested by commercial labs.

1. Seed selection

Preparing environment

In [1]: 
# For inline graphics
%pylab inline
from rdkit import Chem
from rdkit.Chem.Draw import IPythonConsole
from rdkit.Chem import Draw
Draw.DrawingOptions.elemDict[0]=(0.,0.,0.)  # draw dummy atoms in black
from rdkit.Chem import PandasTools
from rdkit.Chem import AllChem as Chem
from rdkit.Chem import DataStructs

# Pandas and Numpy - musthave
import pandas as pd
import numpy as np
%config InlineBackend.figure_format='retina'
# Love this!
import mpld3
mpld3.enable_notebook()

from IPython.display import display
from IPython.display import display_pretty, display_html, HTML, Javascript, Image

pd.options.display.mpl_style = 'default'
pd.options.display.float_format = '{:.2f}'.format
rcParams['figure.figsize'] = 12,9
import warnings
warnings.filterwarnings('ignore')

HTML('''<script>
code_show=true; 
function code_toggle() {
 if (code_show){
 $('div.input').hide();
 } else {
 $('div.input').show();
 }
 code_show = !code_show
} 
$( document ).ready(code_toggle);
</script>
The raw code for this IPython notebook is by default hidden for easier reading.
To toggle on/off the raw code, click <a href="javascript:code_toggle()">here</a>.''')

Populating the interactive namespace from numpy and matplotlib
Out[1]:
The raw code for this IPython notebook is by default hidden for easier reading. To toggle on/off the raw code, click here.

Load NCI60 data (structures and activities)

In [2]: 
gi50 = pd.read_table('/Volumes/public/Users/Alexey/DrugDiscovery/NCI60/DTP_NCI60_RAW.txt', skiprows=7, sep='\t', na_values='-')
not_cells = [x for x in gi50.columns if ':' not in x]
cells = [x for x in gi50.columns if ':' in x]
smiles = pd.read_table('/Volumes/public/Users/Alexey/DrugDiscovery/NCI60/NCIOPENB_SMI', sep='\s+', header=None, index_col=0, names=['hz','smiles'])

print("{0} records from DTP_NCI60 were loaded. {1} structures were loaded from SMILES file".format(len(gi50.index), len(smiles)))

73841 records from DTP_NCI60 were loaded. 237771 structures were loaded from SMILES file

Averaging

Loaded data from DTP_NCI60 may contain multiple records for single compounds. Thus, we will take median GI50 value for each compound in each cell line

In [3]: 
data_melted = pd.melt(gi50, id_vars=not_cells)
data_pivot = pd.pivot_table(data_melted, values='value', index='NSC #',columns='variable', aggfunc=np.median)
print("Data for {0} compounds was loaded".format(len(data_pivot)))

Data for 49847 compounds was loaded
In [4]: 
HTML(data_pivot.iloc[:5,:10].to_html())
Out[4]:
variable BR:BT_549 BR:HS578T BR:MCF7 BR:MDA_MB_231 BR:T47D CNS:SF_268 CNS:SF_295 CNS:SF_539 CNS:SNB_19 CNS:SNB_75
NSC #
1 4.81 4.75 4.83 4.79 5.60 4.78 4.63 4.77 4.05 4.94
17 6.47 4.60 4.73 4.51 6.37 5.75 5.73 6.11 4.00 4.97
26 nan nan 4.97 5.36 4.85 5.36 5.70 5.44 4.84 5.77
89 nan nan nan nan nan 4.00 4.00 4.00 4.00 4.00
171 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00

Filtering for sufficient number of measurements

We will keep only compounds with GI50 values in at least 40 of 60 cell lines

In [5]: 
nans = data_pivot.apply(lambda x: sum(isnan(x)), axis=1)
gi50_ok = data_pivot[nans<20]
print("{0} compounds left for further analysis".format(len(gi50_ok)))

48758 compounds left for further analysis

GI50 distribution properties for each compound

In [6]: 
from scipy import stats
gi50_ok['NSC #'] = gi50_ok.index
data_melted = pd.melt(gi50_ok, id_vars='NSC #')
grouped = data_melted.groupby('NSC #')
def skew(x):
    return stats.skew(x.dropna(), bias=True)
def kurtosis(x):
    return stats.skew(x.dropna(), bias=True)

data_averaged = grouped['value'].agg({
    'mean': lambda x: mean(x.dropna()),
    'median': lambda x: median(x.dropna()),
    'min': lambda x: np.min(x.dropna()),
    'max': lambda x: np.max(x.dropna()),
    'skew': lambda x: stats.skew(x.dropna(), bias=True),
    'sd' : lambda x: np.std(x.dropna()),
    'dif_from_median' : lambda x: sum(abs(x.dropna()-median(x.dropna()))>0.001),
    'kurtosis': lambda x: stats.kurtosis(x.dropna(), fisher=True, bias=True)})
data_averaged['cv'] = data_averaged['sd'] / data_averaged['mean']
HTML(data_averaged.head().to_html())
Out[6]:
min kurtosis max skew sd median dif_from_median mean cv
NSC #
1 4.05 0.08 5.70 0.82 0.35 4.79 58.00 4.89 0.07
17 4.00 -0.96 7.30 0.64 1.00 4.73 58.00 4.93 0.20
26 4.83 -1.40 5.78 -0.16 0.32 5.38 52.00 5.32 0.06
89 4.00 2.40 5.82 1.25 0.38 4.33 50.00 4.34 0.09
171 4.00 -3.00 4.00 0.00 0.00 4.00 0.00 4.00 0.00

Further data filtering

We will require at least 20 values over 60 cell lines to be different from median value (If many values are equal to median value it usually means only one concentration was measured).

In [7]: 
data_diff = data_averaged[(data_averaged['dif_from_median']>20) & (data_averaged['cv'] < 1.0) & (data_averaged['cv']>0)]
print("{0} compounds left for further analysis".format(len(data_diff)))

27168 compounds left for further analysis

Dataset overview in terms of GI50 values distribution

We have to subset our dataset in terms of - unspecificty U, - efficacy E.

To assess unspecificity of compounds, we will consider CV (standard deviation divided my the mean value):

In [8]: 
#matplotlib.rc('text', usetex=False)
#matplotlib.rcParams['text.latex.preamble']=[r"\usepackage{amsmath}"]
density = stats.gaussian_kde(data_diff['cv'])
xx = np.linspace(0, 0.2, 300)
plot(xx, density(xx), lw=2)
fill_between(xx, density(xx),0, alpha=0.2)
xlim([0.,0.3])
data_diff['cv'].hist(bins=200, normed=True)
xlabel(r'CV')
Out[8]:
<matplotlib.text.Text at 0x1197d8bd0>

To assess efficacy E we will consider -log(GI50) values distribution:

In [10]: 
density = stats.gaussian_kde(data_diff['mean'])
xx = np.linspace(data_diff['mean'].min(), data_diff['mean'].max(), 300)
plot(xx, density(xx), lw=2)
fill_between(xx, density(xx),0, alpha=0.2)
data_diff['mean'].hist(bins=50, normed=True)
xlabel('Mean -log10(GI50)')
Out[10]:
<matplotlib.text.Text at 0x119aa69d0>

And scatterplot - CV vs mean -log10(GI50) (some points omitted):

In [12]: 
data_5 = data_diff.iloc[::15,:]
scatter(data_5['mean'], data_5['cv'])
xlabel('Mean -log10(GI50)')
ylabel('CV')
Out[12]:
<matplotlib.text.Text at 0x11a9e0410>

Shortlist generation

Instead of figure of merit function S i would suggest to use figure of merit rules:

  • high efficacy – \(mean(-log10(GI_{50}) > 6\);
  • low specificity – \(CV(-log10(GI_{50}) < 5\%\);
  • toxicity will be calculated later.
In [29]: 
data_seeds = data_diff[(data_diff['mean']>6) & (data_diff['cv'] < 0.05)]
print('{0} compounds selected'.format(len(data_seeds)))

512 compounds selected
In [31]: 
suppl = Chem.SDMolSupplier('/Users/black/Google Drive/NCI60-drugs/March2012_2d_dos.sdf')
compounds = {}
for mol in suppl:
    if mol:
        nnsc = int(mol.GetProp('NSC'))
        compounds[nnsc] = mol
In [47]: 
data_seeds = data_diff[(data_diff['mean']>6) & (data_diff['cv'] < 0.05)]
print('{0} compounds selected'.format(len(data_seeds)))
data_seeds['Mol'] = [(x in compounds) and compounds[x] or '' for x in data_seeds.index]
data_seeds = data_seeds[data_seeds.Mol != '']
print('For {0} compounds molecular structure found'.format(len(data_seeds)))
#PandasTools.AddMoleculeColumnToFrame(data_seeds, smilesCol='smiles', molCol='Mol')
HTML(data_seeds.sort('mean', ascending=False).head().to_html())

512 compounds selected
For 450 compounds molecular structure found
Out[47]:
min kurtosis max skew sd median dif_from_median mean cv Mol
NSC #
670038 10.00 -0.18 11.47 0.23 0.35 10.61 56.00 10.63 0.03 Mol
693565 9.26 1.99 10.60 -1.36 0.30 10.35 60.00 10.33 0.03 Mol
357704 9.42 -0.32 10.73 -0.21 0.27 10.07 60.00 10.02 0.03 Mol
707389 8.34 1.33 10.05 -1.15 0.38 9.60 58.00 9.57 0.04 Mol
611747 7.00 17.43 9.94 -3.73 0.44 9.73 56.00 9.56 0.05 Mol
In [39]: 
pass_input = '/Volumes/public/Users/Alexey/DrugDiscovery/NCI60/20141219_magicbullet_forpass.sdf'
writer = Chem.SDWriter(pass_input)
for idx,row in data_seeds.sort('mean', ascending=False).iterrows():
    mol = row['Mol']
    mol.SetProp('gi50_mean',"%.4f"%row['mean'])
    mol.SetProp('gi50_cv',"%.4f"%row['cv'])
    mol.SetProp('gi50_max',"%.4f"%row['max'])
    mol.SetProp('gi50_median',"%.4f"%row['median'])
    writer.write(mol)
writer.close()
In [45]: 
pass_output = pass_input.replace('.sdf', ' (PASS2A).CSV')
pass_result = pd.read_csv(pass_output, skiprows=8, index_col=0)
In [46]: 
print("For {0} molecules PASS predictions were obtained".format(len(pass_result)))

For 385 molecules PASS predictions were obtained
In [48]: 
effects = [a.strip() for a in open("/Users/black/Dropbox/projects/Best Anticancer Drug/PASS_categories/2A-1-E  Effects.txt").readlines()]
mechanisms = [a.strip() for a in open("/Users/black/Dropbox/projects/Best Anticancer Drug/PASS_categories/2A-2-M  Mechanisms.txt").readlines()]
toxicity = [a.strip() for a in open("/Users/black/Dropbox/projects/Best Anticancer Drug/PASS_categories/2A-4-T  Toxicity.txt").readlines()]
metabolism = [a.strip() for a in open("/Users/black/Dropbox/projects/Best Anticancer Drug/PASS_categories/2A-16-Z Metabolism.txt").readlines()]
transport = [a.strip() for a in open("/Users/black/Dropbox/projects/Best Anticancer Drug/PASS_categories/2A-64-C Transport.txt").readlines()]

pass_toxic = pass_result[[a for a in toxicity if 'Toxic' in a or 'toxic' in a]]
In [81]: 
from sklearn import decomposition, preprocessing
pass_mechanisms = pass_result[mechanisms]
pass_mechanisms_scaled = preprocessing.scale(pass_mechanisms)
pca = decomposition.PCA()
pca.fit(pass_mechanisms_scaled)
Out[81]:
PCA(copy=True, n_components=None, whiten=False)
In [82]: 
X = pca.transform(pass_mechanisms)

MOA Clustering

Selected compounds were subjected for PCA analysis. Each compound was characterised by a vector of PASS activities (Mechanisms section) with at least 20 non-negative values over all selected compounds.

Summary of PCA analysis:

  • PC1 explained 44.1% of variance;
  • PC2 explained 11.6% of variance;
  • 25 PCs explained 90% of variance;
  • 49 PCs explaned 95% of variance.

Results of PCA analysis were subjected to agglomerative hierarchical clustering procedure. 8 clusters were generated.

In [89]: 
Image('PCAclusters.png', width=600)
Out[89]:
In [94]: 
clusters = pd.read_csv('clusters2/clustered.csv', index_col=0)
clusters_grouped = clusters.groupby('clust')
In [141]: 
htmlall = ''
for num, dd in clusters_grouped.groups.items():
    clusterdata_1 = data_seeds.ix[dd,['median','cv','min','max','Mol']]
    clusterdata_1 = pd.merge(clusterdata_1, pass_toxic, left_index=True, right_index=True, how='left')
    htmlall += HTML("<h3>Cluster {0}</h3>".format(num)+clusterdata_1.to_html()).data
In [142]: 
HTML(htmlall)
Out[142]:

Cluster 1

median cv min max Mol Cytotoxic Embryotoxic Genotoxic Pneumotoxic Toxic, respiratory center
NSC #
611747 9.73 0.05 7.00 9.94 Mol 3.11 -2.02 -0.60 -0.55 -0.41
702924 8.20 0.04 7.60 9.28 Mol 12.34 -1.04 -0.90 -0.30 -0.26
671677 8.30 0.03 7.51 8.30 Mol 11.39 -0.70 -0.83 -0.28 -0.26
671678 7.40 0.04 6.46 8.23 Mol 11.16 -0.84 -0.81 -0.27 -0.25
702923 7.27 0.05 6.39 8.15 Mol 8.62 -0.69 -0.75 -0.21 -0.22
671680 6.93 0.04 6.43 7.84 Mol 11.07 -0.86 -0.81 -0.27 -0.25
722656 6.53 0.02 6.22 6.86 Mol 4.58 -0.03 -0.83 -0.45 -0.30

Cluster 2

median cv min max Mol Cytotoxic Embryotoxic Genotoxic Pneumotoxic Toxic, respiratory center
NSC #
665803 8.65 0.05 6.96 9.00 Mol 2.74 -2.53 -1.15 -0.45 -0.29
712199 8.42 0.03 7.11 8.60 Mol 3.15 -2.72 -1.29 -0.48 -0.28
714370 8.33 0.05 6.65 9.00 Mol 2.22 -2.65 -1.25 -0.49 -0.27
665804 8.28 0.03 7.30 8.83 Mol 2.71 -2.84 -1.25 -0.50 -0.34
682345 7.75 0.03 6.50 7.96 Mol 0.15 -2.87 -1.26 -0.39 -0.41
748877 6.03 0.02 5.56 6.33 Mol 0.34 -2.66 -1.13 -0.32 -0.33

Cluster 3

median cv min max Mol Cytotoxic Embryotoxic Genotoxic Pneumotoxic Toxic, respiratory center
NSC #
357704 10.07 0.03 9.42 10.73 Mol 2.06 -0.15 -0.48 -0.29 -0.20
70929 9.20 0.05 8.60 10.50 Mol 1.42 -1.28 -0.47 -0.28 -0.24
641318 9.31 0.05 7.32 10.00 Mol 2.25 0.76 -0.48 -0.30 -0.24
609394 9.08 0.05 8.00 9.83 Mol 5.24 -0.70 -0.13 -0.11 -0.24
603724 8.75 0.02 7.93 9.00 Mol 7.59 -0.36 -0.39 -0.14 -0.21
641319 8.66 0.03 8.25 9.47 Mol 2.25 0.75 -0.48 -0.31 -0.25
21205 8.39 0.02 7.82 8.43 Mol 1.36 -1.14 -0.52 -0.29 -0.25
114781 8.28 0.03 6.89 8.89 Mol 4.84 -1.07 -0.60 -0.22 -0.26
243023 8.12 0.03 7.78 9.43 Mol 2.23 0.41 -0.19 -0.25 -0.28
670121 8.12 0.03 7.53 8.79 Mol 1.71 1.04 -0.39 -0.22 -0.22
328167 8.25 0.03 7.21 8.30 Mol 4.90 -0.59 -0.47 -0.13 -0.19
639655 8.05 0.05 7.44 9.00 Mol 1.78 -0.34 -0.23 -0.23 -0.19
670122 8.06 0.04 6.52 8.98 Mol 1.50 0.46 -0.43 -0.26 -0.27
269146 8.00 0.05 6.23 8.00 Mol 1.33 2.05 -0.42 -0.28 -0.23
269754 7.76 0.04 6.31 9.00 Mol 5.38 -0.49 -0.47 -0.13 -0.19
261045 7.92 0.04 7.00 8.00 Mol 2.28 0.80 -0.31 -0.24 -0.18
136044 7.49 0.03 6.84 7.88 Mol 1.63 0.26 -0.24 -0.19 -0.15
267469 7.62 0.05 5.54 8.18 Mol 2.22 0.72 -0.23 -0.23 -0.21
750154 7.43 0.03 6.78 8.04 Mol 2.74 -1.12 -0.46 -0.24 -0.28
267229 7.34 0.05 6.50 7.76 Mol 1.59 0.54 -0.30 -0.17 -0.19
268239 7.29 0.04 6.52 7.83 Mol 2.28 0.70 -0.34 -0.25 -0.18
295565 6.69 0.05 6.24 7.80 Mol 0.80 -0.84 -0.47 -0.25 -0.24
159628 6.68 0.05 6.06 7.49 Mol 0.26 -0.85 -0.38 -0.25 -0.20
710715 6.64 0.03 6.29 7.46 Mol 1.55 0.08 -0.16 -0.14 -0.17
693700 6.62 0.04 5.88 7.39 Mol 2.02 -1.15 -0.52 -0.25 -0.21
102815 6.50 0.04 5.68 7.15 Mol 0.89 -0.20 -0.08 -0.17 -0.21
708496 6.40 0.03 5.86 7.29 Mol 0.32 -0.90 -0.51 -0.32 -0.25
42076 6.44 0.04 5.34 6.65 Mol 1.71 0.13 0.76 -0.24 -0.22
663567 6.38 0.05 5.37 6.73 Mol 0.72 3.54 -0.85 -0.13 -0.20
626171 6.09 0.04 5.56 6.95 Mol 5.17 -1.24 -0.50 -0.23 -0.27
693702 6.00 0.05 5.52 6.69 Mol 2.10 -1.24 -0.50 -0.26 -0.22
702208 6.03 0.04 5.29 6.62 Mol 1.49 -0.35 -0.23 -0.27 -0.24

Cluster 4

median cv min max Mol Cytotoxic Embryotoxic Genotoxic Pneumotoxic Toxic, respiratory center
NSC #
715408 8.82 0.02 8.27 9.00 Mol 2.22 0.02 -0.40 -0.23 -0.10
291312 8.86 0.05 6.87 9.00 Mol 4.01 -0.34 -0.38 -0.07 -0.13
707148 8.27 0.04 7.10 8.60 Mol 1.75 0.78 -0.30 -0.18 -0.11
138780 8.10 0.02 7.69 8.34 Mol 2.39 2.17 -0.43 -0.16 -0.07
163062 7.95 0.04 6.99 8.59 Mol 1.02 -0.22 -0.29 -0.09 -0.11
266494 8.00 0.04 6.77 8.00 Mol 0.95 -0.54 -0.52 -0.15 -0.14
25485 8.00 0.03 6.89 8.00 Mol 1.76 0.95 -0.41 -0.20 -0.17
177378 7.78 0.02 7.26 8.00 Mol 2.17 2.13 -0.27 -0.09 -0.07
650471 7.78 0.04 7.18 8.30 Mol 2.22 0.29 -0.27 -0.20 -0.14
727038 7.88 0.03 6.95 8.00 Mol -0.36 -0.86 -0.62 -0.26 -0.17
93134 7.62 0.03 7.43 8.23 Mol 2.26 0.26 -0.48 -0.28 -0.17
179180 7.70 0.02 7.22 8.00 Mol 0.57 0.41 -0.45 -0.18 -0.12
751641 7.72 0.03 6.89 8.00 Mol 0.75 -0.61 -0.45 -0.14 -0.16
278571 7.69 0.04 6.82 8.00 Mol 2.47 2.10 -0.30 -0.17 -0.08
688285 7.75 0.04 6.66 8.00 Mol 2.24 0.10 -0.33 -0.20 -0.17
7529 7.60 0.03 6.77 8.00 Mol 1.72 0.97 -0.31 -0.17 -0.10
751642 7.66 0.04 6.54 8.00 Mol 0.81 -0.37 -0.39 -0.14 -0.10
747712 7.61 0.03 6.65 8.00 Mol 0.57 -0.69 -0.59 -0.18 -0.14
713200 7.54 0.04 6.73 8.00 Mol 0.08 -0.27 -0.43 -0.20 -0.07
7534 7.43 0.03 7.01 8.25 Mol 1.56 0.11 -0.47 -0.14 -0.01
238181 7.56 0.04 6.04 7.95 Mol 0.66 -0.54 -0.41 -0.08 -0.13
143925 7.57 0.04 6.65 7.87 Mol 1.78 0.73 -0.23 -0.17 -0.13
135036 7.30 0.03 7.13 8.45 Mol 1.39 0.14 -0.37 -0.16 -0.06
106399 7.48 0.05 6.02 7.78 Mol 3.14 -0.46 -0.64 -0.16 -0.15
117180 7.27 0.03 6.82 7.60 Mol 1.92 0.08 -0.35 -0.14 0.09
95100 7.38 0.04 6.69 7.73 Mol 1.70 1.17 -0.29 -0.18 -0.10
7533 7.33 0.05 6.43 7.86 Mol 2.31 1.56 -0.38 -0.23 -0.15
114340 7.14 0.05 6.36 7.81 Mol 1.13 -0.02 -0.45 -0.12 -0.16
135077 6.69 0.05 6.24 7.50 Mol 1.39 -0.78 -0.50 -0.19 -0.10
112167 6.77 0.02 6.22 7.00 Mol 2.76 -0.57 -0.61 -0.16 -0.15
682561 6.57 0.05 6.03 7.21 Mol 1.73 0.51 -0.36 -0.20 -0.10
39088 6.49 0.04 5.96 7.19 Mol 0.55 0.73 -0.45 -0.18 -0.04
94743 6.52 0.04 6.00 7.42 Mol 2.59 -0.29 -0.70 -0.19 -0.18
179176 6.49 0.04 5.75 6.78 Mol 0.54 0.03 -0.45 -0.18 -0.12
135073 6.42 0.04 5.92 6.78 Mol 2.12 -0.08 -0.38 -0.20 -0.08
706804 6.14 0.03 5.90 6.80 Mol 2.18 1.30 -0.55 -0.28 -0.15
714608 6.22 0.03 5.45 6.40 Mol 2.18 1.30 -0.55 -0.28 -0.15
715768 6.14 0.04 4.89 6.39 Mol 0.62 -0.29 -0.50 -0.13 -0.17

Cluster 5

median cv min max Mol Cytotoxic Embryotoxic Genotoxic Pneumotoxic Toxic, respiratory center
NSC #
670038 10.61 0.03 10.00 11.47 Mol 6.26 -1.47 -0.67 -0.20 -0.19
693565 10.35 0.03 9.26 10.60 Mol 0.01 -1.56 -0.55 -0.16 -0.18
348115 8.99 0.03 8.64 10.00 Mol 1.10 -0.32 0.11 -0.09 -0.20
648910 8.67 0.03 8.43 9.67 Mol 0.15 -0.51 0.01 -0.14 -0.12
52947 8.50 0.04 7.87 9.00 Mol -0.04 -0.95 -0.61 -0.23 -0.23
722518 8.56 0.05 7.00 9.74 Mol -0.81 -2.08 -0.62 -0.23 -0.17
646616 8.44 0.04 7.94 9.72 Mol 0.15 -1.06 -0.59 -0.43 -0.32
681239 8.48 0.03 7.72 8.83 Mol -0.82 -1.91 -0.60 -0.23 -0.18
355461 8.40 0.03 7.88 8.96 Mol 1.47 -0.69 -0.36 -0.08 -0.15
693546 8.34 0.05 7.80 9.91 Mol 0.37 -1.25 -0.32 -0.25 -0.14
699483 8.41 0.04 6.60 8.60 Mol 0.19 -1.46 -0.49 -0.25 -0.16
326408 8.31 0.04 6.57 8.61 Mol -0.04 -1.03 -0.34 -0.19 -0.08
698104 8.19 0.04 7.50 9.62 Mol 0.23 -0.76 -0.11 -0.21 -0.13
670655 8.30 0.04 6.97 8.65 Mol -0.26 -1.32 -0.43 -0.18 -0.15
83950 8.00 0.03 8.00 8.89 Mol -0.05 0.82 -0.04 -0.20 -0.19
700013 8.15 0.01 7.82 8.30 Mol 0.12 -0.80 -0.12 -0.22 -0.14
701747 7.94 0.05 7.26 9.00 Mol -0.09 -1.24 -0.28 -0.27 -0.18
687939 8.14 0.03 7.19 8.30 Mol -0.08 -0.72 -0.26 -0.17 -0.15
400979 7.99 0.05 5.94 8.66 Mol 0.39 -0.49 -0.22 -0.06 -0.11
45383 8.00 0.03 6.89 8.32 Mol -0.05 0.82 -0.04 -0.20 -0.19
691242 8.00 0.03 6.99 8.00 Mol 0.35 -1.28 -0.33 -0.24 -0.13
32743 7.92 0.03 7.06 8.38 Mol 0.61 0.98 -0.29 -0.06 -0.04
651849 8.00 0.03 6.76 8.28 Mol 0.08 -0.88 -0.57 -0.42 -0.32
693540 7.87 0.02 7.51 8.00 Mol 0.20 -1.17 -0.15 -0.24 -0.17
382459 8.00 0.05 6.52 8.00 Mol 0.00 -0.92 -0.36 -0.13 -0.13
648060 7.89 0.03 7.01 8.00 Mol 0.52 -0.54 -0.28 -0.15 -0.15
712399 7.73 0.05 6.48 8.30 Mol 0.34 0.28 -0.09 0.07 -0.05
216128 7.85 0.04 6.88 8.00 Mol 2.19 -0.60 -0.38 -0.06 -0.10
400978 7.78 0.04 6.61 8.42 Mol 0.16 -0.51 -0.19 -0.03 -0.08
743267 7.76 0.04 6.96 8.30 Mol -0.92 -0.91 -0.50 -0.25 -0.18
696126 7.75 0.03 6.60 8.00 Mol 0.34 -0.94 -0.55 -0.29 -0.20
56310 7.74 0.03 7.11 8.00 Mol 1.84 0.18 -0.12 -0.13 -0.04
123111 7.81 0.05 6.77 8.00 Mol 0.61 -0.97 -0.46 -0.15 -0.16
713205 7.63 0.02 7.23 8.00 Mol -0.47 -0.90 -0.64 -0.27 -0.15
106408 7.62 0.03 6.68 8.00 Mol -0.23 -1.31 -0.54 -0.20 -0.21
381837 7.56 0.03 6.97 7.90 Mol -0.07 -0.59 -0.24 -0.16 -0.14
692303 7.47 0.04 6.70 8.00 Mol 1.39 -0.24 -0.30 -0.21 -0.13
39147 7.42 0.04 6.63 8.00 Mol 0.42 0.75 -0.33 -0.04 -0.12
708495 7.48 0.05 5.89 7.93 Mol -0.08 -0.96 -0.42 -0.23 -0.17
89671 7.42 0.04 6.70 8.00 Mol 1.84 0.18 -0.12 -0.13 -0.04
696078 7.43 0.05 6.25 8.00 Mol 0.12 -1.49 -0.60 -0.42 -0.31
692306 7.32 0.05 6.68 8.00 Mol 1.17 -0.36 -0.35 -0.25 -0.17
349156 7.29 0.03 6.70 7.65 Mol -0.22 -0.71 -0.27 -0.15 -0.14
681229 7.36 0.05 6.28 7.88 Mol -0.60 -1.54 -0.48 -0.21 -0.15
745363 7.25 0.04 6.28 7.89 Mol -0.75 -1.35 -0.53 -0.16 -0.15
613009 7.17 0.04 6.24 7.65 Mol 1.21 -1.37 -0.63 -0.22 -0.23
682506 7.12 0.05 6.52 7.90 Mol 0.28 -1.18 -0.38 -0.23 -0.10
697539 7.07 0.05 6.53 8.00 Mol 0.01 -1.56 -0.38 -0.26 -0.20
645578 6.98 0.04 6.58 8.00 Mol -0.36 2.14 0.22 -0.13 -0.09
65380 6.86 0.04 6.45 7.50 Mol 0.27 -0.44 -0.29 -0.11 -0.12
645579 6.85 0.03 6.61 7.67 Mol -0.42 2.19 0.22 -0.12 -0.07
99182 6.84 0.02 6.63 7.28 Mol -0.40 2.23 0.26 -0.11 -0.07
117175 6.83 0.01 6.67 7.11 Mol -0.65 2.04 0.20 -0.16 -0.07
99171 6.82 0.01 6.64 7.15 Mol -0.29 2.30 0.31 -0.12 -0.11
688303 6.78 0.04 6.33 7.49 Mol 0.67 -1.12 0.04 -0.25 -0.16
99154 6.80 0.01 6.61 7.21 Mol -0.42 2.32 0.26 -0.10 -0.01
715991 6.77 0.02 6.70 7.33 Mol -0.11 -0.46 -0.32 -0.26 -0.15
688304 6.72 0.04 6.28 7.56 Mol 0.21 -1.30 -0.44 -0.27 -0.16
325014 6.71 0.04 5.44 7.13 Mol 0.29 -0.10 0.11 -0.21 -0.15
674509 6.68 0.04 6.00 7.27 Mol -0.83 -1.23 -0.77 -0.30 -0.28
665910 6.67 0.05 5.75 7.92 Mol -0.89 -1.49 -0.64 -0.18 -0.17
718798 6.63 0.02 6.47 7.39 Mol 0.42 -0.47 -0.32 -0.14 -0.13
269142 6.64 0.03 5.63 6.98 Mol 2.18 1.98 -0.06 -0.10 -0.08
750440 6.58 0.04 5.94 7.26 Mol 0.84 -0.06 -0.24 -0.17 -0.09
138425 6.54 0.04 5.97 7.27 Mol 0.87 -0.35 -0.13 -0.07 -0.12
705128 6.54 0.03 5.99 7.41 Mol 0.01 1.07 -0.18 -0.11 -0.10
267033 6.50 0.03 5.70 7.17 Mol 2.02 1.36 -0.29 -0.06 -0.04
670851 6.46 0.04 5.91 7.13 Mol -0.43 -1.40 -0.57 -0.19 -0.14
73846 6.49 0.03 5.70 6.81 Mol 2.02 1.36 -0.29 -0.06 -0.04
52141 6.47 0.05 5.66 7.14 Mol 2.20 -0.17 -0.03 0.00 -0.06
354843 6.30 0.05 5.88 7.34 Mol 1.37 -0.24 -0.05 -0.12 -0.17
667931 6.30 0.05 5.27 7.09 Mol -0.23 -1.11 -0.32 -0.19 -0.15
641173 6.35 0.04 5.51 6.60 Mol 0.34 -0.28 -0.10 -0.08 -0.09
17257 6.25 0.05 5.39 7.11 Mol 0.01 0.12 -0.47 -0.11 -0.13
750441 6.19 0.04 5.73 6.95 Mol 0.14 -0.68 -0.50 -0.13 -0.11
650718 6.21 0.05 5.61 6.75 Mol -0.58 -1.29 -0.51 -0.23 -0.16
626371 6.00 0.03 6.00 6.69 Mol 0.36 -0.56 -0.19 -0.02 -0.09
730564 6.09 0.04 5.32 6.93 Mol 1.23 -0.44 -0.21 -0.16 -0.15
676676 6.14 0.04 5.77 7.15 Mol 0.11 -0.56 -0.12 -0.06 -0.12
730563 6.08 0.05 5.64 6.77 Mol 1.21 -0.45 -0.16 -0.14 -0.09
657301 6.06 0.04 5.68 6.87 Mol -0.34 -0.78 -0.43 -0.14 -0.05
746149 6.07 0.05 5.48 6.68 Mol -0.56 -1.14 -0.63 -0.26 -0.20
676767 6.12 0.05 5.34 7.02 Mol -0.07 -0.99 -0.31 -0.21 -0.16
717071 5.96 0.05 5.55 6.70 Mol -0.73 -1.33 -0.46 -0.18 -0.15
645318 5.91 0.05 5.72 6.74 Mol -0.31 -0.71 -0.18 -0.17 -0.09
744469 6.00 0.05 5.43 6.72 Mol -1.13 -1.56 -0.81 -0.31 0.20
643839 5.93 0.05 5.54 6.73 Mol -0.64 2.11 0.14 -0.13 -0.10

Cluster 6

median cv min max Mol Cytotoxic Embryotoxic Genotoxic Pneumotoxic Toxic, respiratory center
NSC #
133114 8.17 0.03 7.62 9.00 Mol 0.61 -0.57 -0.60 -0.27 -0.24
352890 8.03 0.03 7.34 8.90 Mol 0.63 -0.61 -0.33 -0.18 -0.20
675865 7.84 0.04 7.02 8.00 Mol 0.54 -0.65 -0.32 -0.20 -0.18
99843 7.74 0.04 6.63 8.00 Mol 0.78 -0.69 -0.43 -0.24 -0.21
687510 7.72 0.04 6.12 8.00 Mol 0.39 -0.53 -0.65 -0.31 -0.28
750854 7.61 0.03 7.19 8.00 Mol 0.36 -0.73 -0.62 -0.25 -0.24
188491 7.66 0.04 6.63 8.00 Mol 0.43 -0.20 -0.55 -0.29 -0.26
143648 7.44 0.05 7.15 8.66 Mol 0.90 -0.61 -0.44 -0.24 -0.23
175630 7.26 0.04 6.62 7.76 Mol 0.59 -0.65 -0.44 -0.24 -0.20
65346 7.06 0.02 6.75 7.55 Mol 0.90 -0.61 -0.44 -0.24 -0.23
664236 6.81 0.04 6.37 7.76 Mol 0.56 -0.62 -0.29 -0.19 -0.17
122816 6.77 0.03 6.48 7.53 Mol 0.91 -0.31 -0.10 -0.20 -0.22
105827 6.73 0.04 6.22 7.71 Mol 0.81 -0.34 -0.48 -0.26 -0.24
359079 6.66 0.03 6.24 7.33 Mol 0.83 -0.68 -0.46 -0.26 -0.21
180525 6.59 0.04 6.02 7.25 Mol 0.67 -0.22 -0.47 -0.25 -0.22
116282 6.48 0.03 6.04 7.05 Mol 0.62 -0.98 -0.47 -0.27 -0.26
72961 6.42 0.04 5.81 7.30 Mol 1.32 -0.36 -0.42 -0.23 -0.21
367413 6.36 0.04 5.54 6.72 Mol 0.78 -0.65 -0.60 -0.27 -0.26
102816 6.03 0.05 5.10 6.60 Mol 1.12 0.55 -0.35 -0.19 -0.18
65423 5.93 0.04 5.61 6.51 Mol 0.89 -0.35 -0.40 -0.19 -0.18

Cluster 7

median cv min max Mol Cytotoxic Embryotoxic Genotoxic Pneumotoxic Toxic, respiratory center
NSC #
618487 8.00 0.04 7.18 9.22 Mol 0.44 -1.33 -0.45 -0.21 -0.19
638850 7.55 0.05 6.54 8.17 Mol 0.53 -1.24 -0.46 -0.22 -0.21

Cluster 8

median cv min max Mol Cytotoxic Embryotoxic Genotoxic Pneumotoxic Toxic, respiratory center
NSC #
650395 8.51 0.03 7.71 9.02 Mol -0.11 -0.65 -0.18 -0.11 -0.06
247561 8.50 0.04 7.80 9.00 Mol -0.29 -0.82 -0.12 -0.13 -0.04
645806 8.32 0.04 6.89 8.97 Mol -0.18 -0.70 -0.11 -0.12 -0.06
645804 8.31 0.04 8.00 9.33 Mol -0.31 -0.82 -0.11 -0.14 -0.04
645805 8.22 0.04 7.80 9.35 Mol -0.35 -0.83 -0.13 -0.14 -0.09
650396 8.34 0.02 7.68 8.76 Mol -0.26 -0.95 -0.20 -0.11 -0.07
673792 8.03 0.04 6.32 8.66 Mol -0.15 -0.84 -0.14 -0.13 -0.12
645807 7.95 0.05 7.00 8.87 Mol -0.25 -0.67 -0.16 -0.12 -0.02
295505 8.00 0.02 7.20 8.00 Mol -0.33 -0.91 -0.12 -0.13 -0.04
751249 7.98 0.02 7.15 8.00 Mol -0.76 -1.45 -0.41 -0.10 -0.12
711945 8.00 0.03 7.08 8.00 Mol 0.20 -0.87 -0.14 -0.20 -0.12
47147 8.00 0.04 6.60 8.00 Mol -0.65 -1.28 -0.49 -0.18 -0.18
118028 7.88 0.03 6.95 8.00 Mol -0.21 1.29 0.13 -0.02 -0.01
711948 8.00 0.04 6.17 8.00 Mol 0.20 -0.87 -0.14 -0.20 -0.12
667644 7.89 0.03 6.38 8.00 Mol -0.42 -1.02 -0.23 -0.15 -0.04
710558 8.00 0.04 7.01 8.00 Mol -0.50 -1.30 -0.39 -0.21 -0.04
628585 7.78 0.02 7.29 8.00 Mol -0.36 1.62 0.29 -0.07 -0.09
710559 8.00 0.05 6.74 8.00 Mol -0.37 -1.21 -0.45 -0.22 -0.05
32944 7.64 0.03 6.40 8.03 Mol -0.23 -0.89 -0.41 -0.16 -0.05
33669 7.69 0.04 6.49 8.46 Mol -0.35 -0.91 -0.45 -0.16 -0.04
76712 7.67 0.02 7.26 8.00 Mol 0.28 -0.59 -0.29 -0.13 -0.05
60387 7.64 0.04 6.91 8.00 Mol -0.18 -0.58 -0.21 -0.11 -0.04
710550 7.70 0.04 6.72 8.00 Mol -0.76 -1.21 -0.44 -0.23 -0.11
674699 7.77 0.05 6.04 8.00 Mol -0.26 -0.94 -0.20 -0.16 -0.06
668360 7.67 0.05 6.43 8.00 Mol -0.47 -0.63 -0.20 -0.08 -0.06
690634 7.76 0.04 6.60 8.00 Mol -0.24 -0.77 -0.10 -0.10 -0.07
668525 7.71 0.05 6.22 8.00 Mol -0.42 -1.08 -0.23 -0.14 -0.14
743135 7.60 0.04 6.42 8.00 Mol -0.74 -1.59 -0.67 -0.28 -0.18
76557 7.64 0.03 7.00 8.00 Mol -0.39 1.81 0.25 -0.10 -0.08
716802 7.55 0.02 6.84 8.00 Mol -0.01 -0.71 -0.21 -0.09 -0.04
743121 7.58 0.04 6.43 7.98 Mol -0.69 -1.62 -0.66 -0.26 -0.16
744327 7.54 0.04 6.41 8.00 Mol -0.76 -1.62 -0.59 -0.23 -0.19
673793 7.56 0.05 6.16 8.00 Mol -0.17 -0.83 -0.14 -0.17 -0.08
43675 7.57 0.04 6.56 7.91 Mol -0.37 5.27 4.51 -0.06 -0.05
168597 7.52 0.04 6.89 8.37 Mol -0.40 5.28 3.25 -0.08 -0.05
129414 7.51 0.04 6.50 8.00 Mol -0.23 -0.98 -0.38 -0.15 -0.05
76387 7.44 0.05 6.42 8.31 Mol -0.21 -0.57 -0.17 -0.09 -0.02
624548 7.47 0.03 7.00 8.10 Mol 0.07 -1.24 -0.39 -0.17 -0.18
145669 7.44 0.05 6.34 8.00 Mol -0.45 -0.74 -0.23 -0.09 -0.11
752358 7.46 0.05 5.94 7.85 Mol -0.73 -1.40 -0.46 -0.15 -0.20
649890 7.35 0.03 6.51 7.74 Mol 0.34 -0.67 -0.15 -0.17 -0.06
92904 7.31 0.03 6.80 7.84 Mol -0.02 -0.66 -0.17 -0.08 -0.05
41390 7.36 0.03 6.80 7.77 Mol -0.05 -0.86 -0.29 -0.11 -0.08
680735 7.33 0.05 6.36 8.00 Mol -0.02 -0.91 -0.31 -0.11 -0.16
744023 7.38 0.05 6.46 7.73 Mol -0.51 -1.25 -0.52 -0.21 -0.15
185 7.33 0.04 6.66 7.75 Mol 0.19 0.61 -0.35 -0.05 -0.12
659142 7.19 0.03 6.51 7.75 Mol -0.29 0.55 0.07 -0.07 -0.07
100055 7.26 0.04 6.31 8.00 Mol -0.04 -0.79 -0.22 -0.11 -0.05
263434 7.14 0.05 6.23 7.74 Mol -0.20 -0.82 -0.28 -0.15 -0.10
617965 7.04 0.05 6.57 8.00 Mol -0.15 -0.82 -0.28 -0.16 -0.06
617977 7.02 0.04 6.54 7.74 Mol -0.16 -0.86 -0.27 -0.15 -0.05
662191 6.91 0.04 6.55 7.78 Mol -0.20 -0.91 -0.12 -0.16 -0.04
22323 6.92 0.03 6.57 7.77 Mol -0.40 5.28 3.25 -0.08 -0.05
750213 7.04 0.05 5.82 7.55 Mol -0.08 -0.66 -0.25 -0.10 -0.10
742296 6.91 0.05 6.03 7.88 Mol -0.64 -1.44 -0.62 -0.25 -0.18
102980 6.86 0.02 6.66 7.25 Mol -0.39 2.45 0.33 -0.13 -0.05
674500 6.83 0.04 6.39 7.89 Mol -0.45 -1.04 -0.58 -0.23 -0.21
706126 6.84 0.03 6.36 7.38 Mol 0.14 -0.64 -0.29 -0.12 -0.05
751342 6.80 0.04 6.17 7.50 Mol -0.33 -1.25 -0.33 -0.17 -0.11
674504 6.75 0.04 6.11 7.84 Mol -0.62 -1.10 -0.62 -0.21 -0.22
146109 6.74 0.04 6.05 7.71 Mol -0.20 -0.65 -0.19 -0.12 -0.12
674350 6.74 0.04 6.00 7.69 Mol -0.39 -0.52 -0.23 -0.09 -0.09
695589 6.75 0.05 5.74 7.79 Mol 0.20 -1.13 -0.24 -0.21 -0.13
615806 6.73 0.03 6.27 7.67 Mol -0.27 -0.74 -0.11 -0.12 -0.01
658358 6.68 0.05 5.68 7.70 Mol -0.51 -0.81 -0.48 -0.19 -0.14
306365 6.71 0.04 5.73 7.43 Mol -0.28 -0.76 -0.27 -0.15 -0.06
141819 6.73 0.02 6.30 7.04 Mol -0.22 2.70 0.40 -0.03 -0.03
747458 6.66 0.05 5.89 7.38 Mol -0.52 -1.41 -0.62 -0.22 -0.20
628592 6.72 0.03 5.87 7.44 Mol -0.45 1.49 0.27 -0.07 -0.10
736597 6.62 0.04 5.87 8.00 Mol -0.27 -1.11 -0.20 -0.20 -0.12
220589 6.68 0.04 5.94 7.45 Mol -0.55 -0.73 0.19 -0.14 -0.12
673789 6.65 0.05 5.79 7.61 Mol -0.33 -0.91 -0.12 -0.13 -0.04
27445 6.59 0.05 5.85 7.39 Mol -0.14 -0.48 -0.15 -0.05 -0.09
99148 6.61 0.04 5.85 7.36 Mol -0.26 -0.46 -0.14 -0.05 -0.03
47731 6.60 0.04 5.82 7.32 Mol -0.27 -0.40 -0.13 -0.08 -0.03
332436 6.61 0.05 5.74 7.62 Mol -0.64 -1.38 -0.40 -0.16 -0.17
741299 6.63 0.05 5.83 8.00 Mol -0.72 -1.07 -0.38 -0.19 -0.18
178264 6.68 0.05 5.67 7.36 Mol -0.19 -0.06 -0.09 -0.03 -0.02
178296 6.61 0.04 5.67 7.23 Mol -0.32 -0.47 -0.13 -0.12 -0.07
628594 6.65 0.05 5.80 8.00 Mol -0.45 0.56 -0.21 -0.06 -0.12
619324 6.69 0.04 5.85 7.16 Mol -0.21 -0.12 -0.12 -0.03 0.01
674351 6.58 0.03 6.01 7.24 Mol 0.10 -0.41 -0.15 -0.11 -0.08
747460 6.54 0.04 5.98 7.27 Mol -0.55 -1.36 -0.59 -0.21 -0.19
743122 6.62 0.03 5.77 7.24 Mol -1.08 -1.74 -0.75 -0.34 -0.19
26113 6.59 0.05 5.61 8.00 Mol -0.24 0.00 -0.03 -0.07 -0.06
633555 6.63 0.04 6.00 7.00 Mol 0.11 -0.34 -0.10 0.01 -0.07
679748 6.66 0.05 5.72 7.70 Mol 0.17 -0.26 0.04 -0.04 -0.07
15013 6.68 0.05 5.74 7.16 Mol -0.11 -0.36 0.01 -0.10 -0.10
631392 6.64 0.05 5.66 7.39 Mol 0.26 -0.97 -0.19 -0.16 -0.21
747457 6.56 0.04 5.87 7.08 Mol -0.57 -1.41 -0.61 -0.22 -0.20
30846 6.59 0.03 5.81 6.83 Mol -0.21 -0.47 -0.22 -0.09 -0.00
349155 6.50 0.04 6.00 7.31 Mol 0.03 -0.47 -0.14 -0.14 -0.14
744332 6.59 0.03 5.71 6.86 Mol -0.64 -1.36 -0.54 -0.21 -0.14
743134 6.59 0.04 5.98 7.35 Mol -1.09 -1.75 -0.76 -0.34 -0.20
617987 6.48 0.05 6.03 7.36 Mol -0.29 -1.06 -0.26 -0.15 -0.08
740383 6.63 0.05 5.78 7.24 Mol -0.47 -0.95 -0.46 -0.14 -0.17
742543 6.58 0.05 5.72 7.21 Mol -0.05 -0.72 -0.10 -0.10 -0.12
679835 6.55 0.04 5.82 7.08 Mol -0.52 -0.90 -0.41 -0.12 -0.15
745357 6.66 0.04 5.76 6.77 Mol -0.14 -0.61 -0.47 -0.17 -0.13
299863 6.52 0.02 6.15 7.07 Mol -0.14 -0.33 -0.18 0.00 -0.04
744336 6.56 0.04 5.55 6.92 Mol -0.50 -1.57 -0.55 -0.23 -0.16
664215 6.54 0.05 5.92 7.40 Mol 0.38 0.05 0.17 -0.13 -0.12
15886 6.49 0.03 5.88 7.22 Mol -0.21 -0.44 -0.04 -0.08 -0.05
744613 6.50 0.04 5.69 7.25 Mol -0.96 -1.41 -0.49 -0.23 -0.21
745358 6.59 0.05 5.75 6.75 Mol -0.14 -0.82 -0.47 -0.16 -0.11
265959 6.49 0.04 5.14 7.27 Mol -0.28 -0.71 -0.22 -0.06 -0.13
205105 6.50 0.05 5.56 7.40 Mol -0.49 -0.72 -0.24 -0.17 -0.19
5013 6.48 0.03 5.82 6.77 Mol -0.17 -0.52 -0.20 -0.09 -0.12
736379 6.45 0.03 5.92 6.83 Mol -0.19 -0.98 -0.23 -0.20 -0.11
658573 6.48 0.04 5.54 6.98 Mol 0.33 -0.13 0.10 -0.04 -0.07
748382 6.46 0.03 6.00 6.79 Mol -0.80 -1.42 -0.46 -0.21 -0.17
671196 6.41 0.03 6.08 7.22 Mol 0.29 -0.22 -0.00 -0.13 -0.11
702015 6.48 0.04 5.78 6.89 Mol -0.60 -1.47 -0.51 -0.18 -0.16
661422 6.37 0.05 5.82 7.00 Mol 0.27 -0.53 -0.20 -0.13 -0.09
628589 6.47 0.04 5.78 6.86 Mol -0.37 2.52 -0.06 -0.07 -0.09
745355 6.45 0.04 5.79 7.16 Mol -0.53 -1.42 -0.41 -0.23 -0.16
745939 6.41 0.04 5.45 7.19 Mol -0.70 -0.95 -0.36 -0.16 -0.17
242557 6.40 0.04 5.90 7.19 Mol -0.25 -0.24 -0.22 0.15 -0.04
723568 6.47 0.04 4.93 6.87 Mol -0.61 -1.33 -0.54 -0.19 -0.17
37187 6.45 0.04 5.47 6.97 Mol -0.31 -0.52 -0.18 -0.13 -0.09
682362 6.39 0.03 5.72 6.86 Mol -0.19 -0.73 -0.34 -0.13 -0.09
658355 6.37 0.04 5.91 7.43 Mol -0.53 -0.79 -0.33 -0.16 -0.13
30205 6.40 0.05 5.83 7.38 Mol -0.20 -0.62 -0.26 -0.12 -0.05
103837 6.42 0.03 5.72 6.71 Mol -0.42 0.48 -0.24 -0.12 -0.10
718169 6.44 0.05 5.56 7.02 Mol -0.52 -1.04 -0.34 -0.20 -0.12
370589 6.42 0.04 5.55 6.65 Mol -0.56 -1.40 -0.41 -0.14 -0.17
744989 6.40 0.05 5.52 6.73 Mol -0.72 -1.61 -0.58 -0.23 -0.19
751286 6.40 0.03 5.29 6.61 Mol -0.55 -1.46 -0.44 -0.15 -0.17
33409 6.38 0.04 5.59 6.75 Mol -0.29 -0.56 -0.39 -0.20 -0.13
134754 6.39 0.03 5.67 6.69 Mol -0.26 -1.03 -0.38 -0.15 -0.05
37608 6.41 0.04 5.45 6.76 Mol -0.20 -0.49 -0.06 -0.08 -0.06
69603 6.35 0.04 5.58 7.28 Mol -0.27 -0.87 -0.24 -0.10 -0.12
658370 6.37 0.04 5.53 6.90 Mol -0.46 -0.98 -0.36 -0.15 -0.06
724448 6.44 0.05 5.71 6.80 Mol -0.29 -0.79 -0.28 -0.08 -0.11
1010 6.37 0.03 5.75 6.63 Mol -0.43 -1.10 -0.21 -0.09 -0.10
726368 6.34 0.04 5.54 6.82 Mol -0.56 -1.50 -0.48 -0.18 -0.16
751165 6.37 0.04 5.62 6.87 Mol -0.77 -1.21 -0.46 -0.21 -0.16
3905 6.35 0.04 5.15 6.79 Mol -0.12 -0.37 -0.09 -0.02 -0.05
335307 6.38 0.04 5.48 6.89 Mol -0.38 -0.08 -0.42 -0.14 -0.14
143103 6.35 0.05 5.44 7.21 Mol -0.40 -1.13 -0.50 -0.20 -0.17
68088 6.37 0.04 5.43 6.74 Mol -0.60 -1.00 -0.38 -0.16 -0.14
33435 6.34 0.03 5.77 6.63 Mol -0.22 -0.58 -0.19 -0.12 -0.04
103517 6.37 0.04 5.47 6.98 Mol -0.56 -0.94 -0.27 -0.17 -0.18
527347 6.35 0.03 5.63 6.59 Mol -0.62 -0.56 -0.32 -0.11 -0.15
658373 6.31 0.04 5.66 6.94 Mol -0.56 -0.95 -0.32 -0.18 -0.04
81947 6.34 0.04 5.51 6.66 Mol -0.27 -0.48 -0.27 -0.11 -0.11
736606 6.30 0.04 5.67 6.84 Mol -0.45 -0.89 -0.29 -0.13 -0.13
67478 6.36 0.05 4.93 6.72 Mol -0.43 -0.83 -0.28 -0.11 -0.12
747459 6.29 0.04 5.57 6.90 Mol -0.70 -1.42 -0.57 -0.23 -0.21
165714 6.29 0.04 5.69 7.42 Mol -0.20 -0.62 -0.26 -0.12 -0.05
617972 6.28 0.03 5.79 6.62 Mol -0.12 -0.77 -0.29 -0.13 -0.04
332996 6.29 0.05 5.35 7.03 Mol -0.71 -1.18 -0.33 -0.13 -0.15
33461 6.23 0.04 5.68 6.71 Mol -0.15 -0.46 -0.19 -0.09 -0.03
12454 6.27 0.04 5.60 6.63 Mol -0.07 -0.47 -0.15 -0.12 -0.07
26110 6.29 0.04 5.64 6.64 Mol -0.36 -0.04 -0.06 -0.09 -0.04
33577 6.26 0.04 5.82 7.04 Mol -0.26 -0.62 -0.24 -0.14 -0.04
749673 6.25 0.05 5.37 7.17 Mol -0.71 -1.29 -0.44 -0.29 -0.13
674611 6.23 0.04 5.81 7.00 Mol 0.27 -0.20 -0.17 -0.11 -0.09
13006 6.22 0.04 5.30 6.67 Mol -0.09 -0.86 -0.32 -0.17 -0.13
67480 6.22 0.05 5.65 6.70 Mol -0.42 -0.81 -0.28 -0.10 -0.13
83459 6.25 0.05 5.20 6.59 Mol -0.56 -0.87 -0.36 -0.14 -0.12
745938 6.20 0.05 5.14 6.67 Mol -1.04 -1.30 -0.58 -0.23 -0.24
678913 6.13 0.04 5.52 6.84 Mol -0.28 -0.81 -0.32 -0.19 -0.07
617135 6.13 0.03 5.58 6.65 Mol -0.60 -0.83 -0.27 -0.12 -0.10
26112 6.10 0.05 5.67 6.78 Mol -0.27 -0.08 -0.06 -0.05 -0.06
674104 6.13 0.04 5.57 6.64 Mol -0.68 -1.20 -0.34 -0.15 -0.16
748265 6.20 0.05 5.34 6.59 Mol -0.61 -1.49 -0.61 -0.22 -0.18
641170 6.15 0.04 5.61 6.64 Mol 0.20 -0.19 -0.13 -0.11 -0.07
11515 6.15 0.04 5.75 6.51 Mol -0.29 -0.88 -0.40 -0.22 -0.09
641175 6.13 0.04 5.45 6.97 Mol 0.11 -0.23 -0.02 -0.07 -0.05
671197 6.09 0.03 5.61 6.62 Mol 0.34 -0.34 0.03 -0.11 -0.10
71795 6.11 0.04 5.63 6.86 Mol 0.28 -0.71 0.06 -0.04 -0.14
708472 6.11 0.03 5.63 6.51 Mol 0.63 -0.86 -0.29 -0.10 -0.06
742293 6.10 0.05 5.55 7.11 Mol -0.65 -1.47 -0.62 -0.25 -0.18
741425 6.05 0.05 5.47 6.76 Mol -0.36 -0.76 -0.33 -0.10 -0.12
747884 5.92 0.05 5.72 6.82 Mol -0.70 -1.47 -0.38 -0.15 -0.20
710393 5.95 0.05 5.69 6.98 Mol -0.34 -0.87 -0.28 -0.12 -0.11
747199 6.05 0.04 5.60 6.60 Mol -0.99 -1.39 -0.52 -0.23 -0.21
617962 6.03 0.03 5.73 6.56 Mol -0.10 -0.88 -0.30 -0.13 -0.07
617969 6.05 0.04 5.32 6.65 Mol -0.21 -0.95 -0.33 -0.15 -0.07
13002 5.99 0.03 5.76 6.53 Mol -0.13 -0.56 -0.26 -0.10 -0.07
5283 6.00 0.05 5.48 6.73 Mol -0.39 5.89 2.34 -0.06 -0.05
656711 6.02 0.05 5.49 6.66 Mol -0.28 -0.67 -0.43 -0.11 -0.11
641172 6.02 0.05 5.44 6.65 Mol 0.13 -0.25 -0.11 -0.06 -0.05
56618 5.99 0.05 5.65 6.69 Mol -0.34 -0.74 -0.41 -0.22 -0.06
658354 6.09 0.05 5.43 6.61 Mol -0.56 -0.86 -0.33 -0.15 -0.11
661820 6.03 0.05 5.45 6.93 Mol -0.59 -1.16 -0.42 -0.13 -0.08
740043 6.04 0.04 5.43 6.57 Mol -0.71 -1.37 -0.60 -0.22 -0.15
675967 5.92 0.04 5.55 6.63 Mol 0.07 -0.49 -0.09 -0.12 -0.12
747198 5.95 0.05 5.49 7.02 Mol -0.96 -1.41 -0.49 -0.23 -0.21
617960 5.95 0.05 5.66 7.30 Mol -0.05 -0.82 -0.26 -0.16 -0.04
658356 6.00 0.05 5.25 6.66 Mol -0.60 -0.85 -0.40 -0.18 -0.04
674067 5.85 0.05 5.70 6.75 Mol -0.55 -1.22 -0.46 -0.18 -0.15